Automated Program Repair Using Generative Models for Code Infilling
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A4 Artikkeli konferenssijulkaisussa
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Date
2023
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en
Pages
798–803
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Artificial Intelligence in Education : 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings, Lecture Notes in Computer Science, Volume 13916
Abstract
In educational settings, automated program repair techniques serve as a feedback mechanism to guide students working on their programming assignments. Recent work has investigated using large language models (LLMs) for program repair. In this area, most of the attention has been focused on using proprietary systems accessible through APIs. However, the limited access and control over these systems remain a block to their adoption and usage in education. The present work studies the repairing capabilities of open large language models. In particular, we focus on a recent family of generative models, which, on top of standard left-to-right program synthesis, can also predict missing spans of code at any position in a program. We experiment with one of these models on four programming datasets and show that we can obtain good repair performance even without additional training.Description
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Citation
Koutcheme, C, Sarsa, S, Leinonen, J, Hellas, A & Denny, P 2023, Automated Program Repair Using Generative Models for Code Infilling . in N Wang, G Rebolledo-Mendez, N Matsuda, O C Santos & V Dimitrova (eds), Artificial Intelligence in Education : 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings . Lecture Notes in Computer Science, vol. 13916, Springer, pp. 798–803, International Conference on Artificial Intelligence in Education, Tokyo, Japan, 03/07/2023 . https://doi.org/10.1007/978-3-031-36272-9_74